Chong Chie, J. A. K. H. | bioRxiv Science

bioRxiv2026

Neuro-Metabolic and Vascular Dysregulation are Functional Biomarkers of Acquired Brain Injury

Emerging clinical and neuropathological evidence suggests a direct link between traumatic brain injury (TBI) and neurodegenerative diseases (ND), but the mechanisms by which TBI contributes to ND onset and progression remain unclear. To bridge this gap, the NINDS TBI-ADRD consortium developed a diagnostic framework to guide the design and evaluation of TBI-ADRD experiments using translational neuroimaging (DCE-MRI, 18F-FDG-PET) and advanced image-processing pipelines. Neuro-metabolic and vascular dysregulation (MVD) and metabolic functional connectomics (MFC) were evaluated in 8-month C57BL/6J mice at 30- and 60-days post-injury (DPI). Both MVD and MFC revealed significant changes at 30- and 60-DPI that were sex- and injury side-dependent. Female mice showed greater regional MVD alterations, while MFC identified sex-dependent changes at 30-DPI that diminished by 60-DPI. These findings suggest that a single mid-life closed-head injury induces sexually dimorphic MVD and brain network remodeling, supporting the potential of MVD as a sensitive biomarker for improved clinical monitoring post-TBI.

neuroscience↗

bioRxiv2026

Multidomain Analysis of Clinical Cognitive Assessments and Imaging Data in Alzheimer's Disease Accurately Predicts Disease Stage and Grade Independent of Amyloid and Tau

BackgroundIndividual clinical cognitive assessments (CCA) for Alzheimers disease (AD) provide broad disease stratification but are limited in sensitivity and specificity, requiring integration of multiple CCA for optimal disease staging. Recent work from our lab suggests that neuro-metabolic and vascular dysregulation (MVD) occurs early in AD, prior to clinical symptoms, and may provide higher sensitivity and specificity than CCA alone. In this study, we combined three widely accepted CCA with MVD readouts and developed a multimodal ensemble machine learning approach across the AD spectrum to predict disease stage and grade. MethodsAD subjects (N=372) across the disease spectrum with imaging (PET:18F-FDG, MRI:T1w, T2 FLAIR, ASL) and CCAs (ADAS-Cog, CDR, MoCA) data were analyzed from ADNI. Imaging data were registered to MNI152+, z-scored relative to cognitively normal controls, and processed for MVD. A clinical-set-enrichment analysis (CSEA) was developed to link regional brain changes with CCA scores, map changes to functional categories, project them into a 3D Cartesian space, and model trajectories, thus revealing at-risk and resilient regions. In addition, an ensemble machine-learning approach was utilized for disease stage classification, and a disease grading scheme across the AD spectrum was developed to further stratify within disease stages. FindingsRegional data followed an MVD pattern across AD stages stratified by CSEA scores. Females showed greater stage separation along the CCA axis within each region, indicating faster disease progression. Moreover, progression in at-risk brain regions (e.g., mid- and inf-temporal gyri, amygdala) was associated with longer disease path lengths, whereas progression in resilient brain regions (supramarginal gyrus) was not. Moreover, our classification and grading approach can predict AD stage and grade independent of amyloid-beta and tau with high precision and accuracy. InterpretationA framework was developed to evaluate MVD and CCA variations across the AD spectrum, thereby distinguishing at-risk and resilient brain regions. Distinct disease trajectories were identified, and a new data-driven grading scheme was proposed to highlight the potential for precision medicine and therapeutic evaluation. FundingNIH T32AG071444

neuroscience↗